Year: 2025
Type: article
Abstract: High Resolution Image Download MS PowerPoint Slide This work introduces a novel machine learning integral-based self-consistent field method, called mi SCF, designed for the efficient prediction of molecular electronic structures. By incorporating detailed information from the atomic information and geometric features to predict the molecular Fock matrix, this approach enhances data sharing among similar systems and facilitates effective data transfer within chemically analogous systems. This enables precise predictions of various electronic properties of the systems with only a small amount of training data, significantly reducing computational costs while maintaining high accuracy. Testing results on some representative small molecules, H 2 and H 2 O chain as well as ice structures, demonstrate that mi SCF shows good performance in the prediction of energies, wave function, and electron densities, showcasing high precision, efficiency, and good transferability. Thus, the mi SCF method provides an efficient tool for quantum chemical calculations, laying a solid foundation for further applications in potential energy surface construction, ab initio molecular dynamics, and chemical reaction simulations.
Source: JACS Au
Authors: Hongcai Liu, Shuai Guan, Zhuofan Wang, Zhongjun Zhou, Zexing Qu, Wenjian Liu
Institutions: Jilin University, Shandong University
Language: English
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,500
Awards: G255001997, G5318152666
OpenAlex ID: W4415443377